Screeners to Predict Opioid Misuse in Patients With Chronic Pain

"These instruments offer an important and impartial alternative to methods that do not reliably classify individuals according to risk, including clinical interviews."

It is estimated that chronic pain affects up to one-quarter of adults in industrialized nations.1 Opioids represent an effective treatment option for many patients with chronic pain. However, rates of opioid misuse and related overdoses have soared in recent years in the United States, highlighting the importance of screening tools to identify patients at high risk for adverse events from opioid use.2

For patients determined to be at an elevated risk for opioid-related overdose, alternative therapies or increased monitoring may be indicated. “These instruments offer an important and impartial alternative to methods that do not reliably classify individuals according to risk, including clinical interviews, provider observation of problematic behaviors, and urine toxicology screening,” noted the authors of a review on the topic recently published in the Journal of Pain.3

Despite their potential utility for predicting opioid misuse, there are several ways in which the results of screening tests, such as the commonly used Revised Screener and Opioid Assessment for Patients with Pain (SOAPP-R), may be misanalyzed. Avoiding these pitfalls requires a basic understanding of the development and validation process for screeners intended to predict certain behaviors.

As screeners for opioid misuse are designed to assess tendencies (ie, “people with the attribute tend to behave in particular ways or tend to have other, related characteristics”), results from such tests will inevitably include false positives and false negatives. When a screening test is in development, the main task is to maximize the hit rate, which is the number of accurately classified individuals. During development, the “screener is being evaluated relative to an established benchmark test to determine how well the screener can detect the presence or absence of the attribute at the present time in relation to that benchmark,” the authors explained.

Four measures of diagnostic efficiency must be determined: sensitivity, specificity, positive predictive value, and negative predictive value.4 Screener developers must decide whether to emphasize sensitivity over specificity, or vice versa, after considering the benefits of various cut scores to distinguish individuals who do or do not have the attribute being assessed. A cut score that is too low will result in a high number of false positives, whereas a cut score that is too high will lead to a high rate of false negatives. The developers of the SOAPP-R chose to emphasize sensitivity over specificity (81% and 68%, respectively)5 by using a cut score of 18.

The prevalence of a condition or behavior in a population (the base rate) also influences the predictive power of a screener. As an example, with an assumed 3% base rate of opioid misuse in a population of chronic pain patients, positive predictive value is extremely low, whereas negative predictive value is excellent. At this rate, the SOAPP-R is “best used to identify those who will go on to use opioids in a non-aberrant manner and should not alone be used to identify those who will go on to use opioids aberrantly,” noted the authors of the review. With an assumed 50% base rate, the positive predictive value and negative predictive value are both high and may be used to identify those who are likely to misuse opioids as well as those who are unlikely to exhibit such behaviors.

The authors offer the following points regarding the effective development and use of screeners to predict opioid misuse.

With proper use, screeners can be helpful in predicting risk and provide a more objective tool than clinical assessment.

Sensitivity and specificity should not be confused with predictive values. The 81% accuracy of the SOAPP-R, for instance, refers to its sensitivity, not its predictive value. “Therefore positive results should be interpreted with caution and should not be the primary driver behind a recommendation not to prescribe,” advise the review authors. Further steps might include additional testing, closer monitoring, or a lower initial dosage.

Diagnostic efficiency statistics are not fixed properties and should be calculated for each new study sample.

The diagnostic system affects diagnostic efficiency. There is currently no gold standard approach to comparing screener results for opioid disorder. The “lack of a universal benchmark test is perhaps the greatest barrier to improving the predictive validity” of screening tests for opioid misuse.

Overall, using screeners to predict opioid misuse in patients with chronic pain requires the consideration of several key factors. Clinicians should enact fair monitoring practices and may refer patients to substance abuse counseling if a higher risk is identified. However, it is important to “keep in mind the stigmatizing effect of assuming at-risk status when the rate of false positives is high,” the authors noted. This may be especially relevant for African American patients with chronic pain, as they are more likely to be closely monitored and referred for substance abuse evaluation.6

To confirm the potential of a screening test to predict aberrant opioid use in a reliable manner, long-term monitoring of patients is essential, as no diagnostic test to confirm results from screeners is available. Such follow-up could also serve to increase the clinic’s data on base rates and could ultimately inform clinic protocols for interpreting screeners in patients on long-term opioid treatment.